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Notes and Conventions

Notes

This is an example R Markdown report to demonstrate the workflow for building a “data memo” or report object in parallel with the conduct of a RsNLME analysis.
We use html output and “tabsets” within the RMarkdown document to show how RsNLME can be used together with R markdown to produce very dense, interactive data summaries that capture a large amount of data in well organized, easily shared delivery mechanism.
We also show how RsNLME shiny apps generate code specific to your analysis problem that allows you to get the job done, even if you’re not an R expert!

Conventions used in this document

We’ve used blue text boxes to indicate descriptive information

We’ve used yellow text boxes to indicate results

Green text boxes provide key messages from the analysis results
Example: This is a key message statement

# Analysis ID     : Project XYZ Population PK Analysis
# Purpose         : Summarize Base PK model
# Other Info      : Define your own!

pkdata <- read.csv("pkdata.csv")

Objectives

The purpose of this data memo is to provide a summary of the base population pharmacokinetic model fit for study XYZ, and to summarize simulated exposure target attainment as measured by Cmax and AUC24 following the first administered dose of study drug.

Study Design

100 healthy and infected male and female subjects were enrolled in this study at 3 centers under inpatient and outpatient settings
Subjects were administered 100 mg of Drug XYZ intravenously at time=0 and time=24
Blood samples were obtained at 0, 0.25, 0.5, 1, 2, 4, 6, 8, 12, and 24 hours after the first dose, and 12, and 24 hours after the second dose of Drug XYZ
Covariates in the dataset include baseline subject weight, age, creatinine clearance, gender, status (healthy or infected) and study center (3 sites), and setting (inpatient or clinic).

Methods

Data analysis was conducted using the population pharmacokinetic program RsNLME version 1.1.0 (https://certara.github.io/R-RsNLME/index.html)
A two-compartment model with bolus input and first-order elimination from the central compartment was used to model the time course of drug XYZ plasma concentrations. The parameters of this model were clearance (Cl), volume of distribution of the central compartment (V), volume of distribution of the peripheral compartment (V2), and intercompartmental clearance (Cl2)
Body weight was included as a covariate on Cl and V based on previous modeling work
The base model was used to simulate individual concentration time profiles for 100 replicates of the study design, and AUC24 and Cmax were derived and summarized relative to target exposure thresholds

Overview of the data

Summary

Elimination profile appears to be bi-exponential
Body weight and gender appear to influence PK

Categorical covariate summary

catcovsumm <- readRDS(file='catcovsumm.RDS')
catcovsumm

Variable	Center
Variable	AB123456, N = 12¹	AB567765, N = 50¹	AB765432, N = 38¹
SEX
Male	7 (58%)	22 (44%)	17 (45%)
Female	5 (42%)	28 (56%)	21 (55%)
STATUS
HEALTHY	7 (58%)	10 (20%)	13 (34%)
INFECTED	5 (42%)	40 (80%)	25 (66%)
SETTING
Clinic	0 (0%)	50 (100%)	0 (0%)
Inpatient	12 (100%)	0 (0%)	38 (100%)
¹ n (%)

Continuous covariate summary

contcovsumm <- readRDS(file='contcovsumm.RDS')
contcovsumm

Variable	Center
Variable	AB123456, N = 12¹	AB567765, N = 50¹	AB765432, N = 38¹
WT	74 (28) [44-126]	69 (26) [34-121]	74 (28) [34-153]
AGE	58 (25) [31-93]	57 (18) [27-90]	49 (13) [27-90]
CRCL	91 (10) [78-112]	88 (11) [66-112]	91 (11) [71-114]
¹ Mean (SD) [Minimum-Maximum]

Mean profiles Linear

meanlinplot <- readRDS(file='meanlinplot.RDS')
meanlinplot

Mean profiles Log

bi-exponential disposition

meanlogplot <- readRDS(file='meanlogplot.RDS')
meanlogplot

Facet Gender Linear

Gender influence on PK with higher DV’s in females

meanlinplotbysex <- readRDS(file='meanlinplotbysex.RDS')
meanlinplotbysex

Facet WT Linear

WT influence on PK with higher DV’s in lower weight subjects

meanlinplotbywt <- readRDS(file='meanlinplotbywt.RDS')
meanlinplotbywt

Base Model Development

Summary

A 2 compartment model with IV bolus input was used to fit the data
Convergence was achieved with successful estimation of parameter standard errors
Model fit diagnostics show acceptable fit

Model Description

A 2 compartment model with IV bolus input was used to fit the data
Model parameters central clearance (Cl), central volume (V), inter-compartmental clearance (Cl2), and peripheral compartment volume (V2)
Body weight covariate on Cl and V (parameters dCldWT and dVdWT, respectively)
Between subject variability terms on Cl and V
Proportional residual error model

Structural PK Model

## View the updated model 
model<-readRDS("model.RDS")
print(model)

## Loading required package: Certara.RsNLME

## 
##  Model Overview 
##  ------------------------------------------- 
## Is population     :  TRUE
## Model Type        :  PK
## 
##  PK 
##  ------------------------------------------- 
## Parameterization  :  Clearance
## Absorption        :  Intravenous
## Num Compartments  :  2
## Dose Tlag?        :  FALSE
## Elimination Comp ?:  FALSE
## Infusion Allowed ?:  FALSE
## Sequential        :  FALSE
## Freeze PK         :  FALSE
## 
##  PML 
##  ------------------------------------------- 
## test(){
##     cfMicro(A1,Cl/V, Cl2/V, Cl2/V2)
##     dosepoint(A1)
##     C = A1 / V
##     error(CEps=0.2)
##     observe(CObs=C * ( 1 + CEps))
##     stparm(V = tvV * ((WT/70)^dVdWT)   * exp(nV))
##     stparm(Cl = tvCl * ((WT/70)^dCldWT)   * exp(nCl))
##     stparm(V2 = tvV2)
##     stparm(Cl2 = tvCl2)
##     fcovariate(WT)
##     fcovariate(AGE)
##     fcovariate(CRCL)
##     fcovariate(SEX())
##     fixef( tvV = c(,80,))
##     fixef( tvCl = c(,6,))
##     fixef( tvV2 = c(,100,))
##     fixef( tvCl2 = c(,9,))
##     fixef( dVdWT(enable=c(0)) = c(,0,))
##     fixef( dCldWT(enable=c(1)) = c(,0,))
##     ranef(diag(nV,nCl) = c(0.1,0.1))
## }
## 
##  Structural Parameters 
##  ------------------------------------------- 
##  V Cl V2 Cl2
##  ------------------------------------------- 
## Observations:
## Observation Name :  CObs
## Effect Name      :  C
## Epsilon Name     :  CEps
## Epsilon Type     :  Multiplicative
## Epsilon frozen   :  FALSE
## is BQL           :  FALSE
##  ------------------------------------------- 
##  Column Mappings 
##  ------------------------------------------- 
## Model Variable Name : Data Column name
## id                  : ID
## time                : TIME
## A1                  : AMT
## WT                  : WT
## AGE                 : AGE
## CRCL                : CRCL
## SEX                 : SEX( male=0 female=1 )
## CObs                : DV

GOF Plots

DV vs PRED

dvpred <- readRDS("dvpred.RDS")
dvpred

DV vs iPRED

dvipred <- readRDS("dvipred.RDS")
dvipred

CWRES vs TIME

cwresidv <- readRDS("cwresidv.RDS")
cwresidv

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

CWRES vs PRED

cwrespred <- readRDS("cwrespred.RDS")
cwrespred

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ETA vs WT

etacovcont <- readRDS("etacovcont.RDS")

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

etacovcont

## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using formula 'y ~ x'

ETA vs SEX

etacovcat <- readRDS("etacovcat.RDS")
etacovcat

Parameter Estimates

Overall Run Summary

tableoverall <- readRDS("tableoverall.RDS")
tableoverall

Table Overall
RetCode	Condition	LL	-2LL	AIC	BIC	nParm	nObs	nSub
1	210.44	1,115.44	-2,230.88	-2,212.88	-2,167.85	9	1,100	100
Source: script.R

Thetas

tabletheta <- readRDS("tabletheta.RDS")
tabletheta

Table Theta
Name	Label	Value	SE	RSE%	2.5% CI	97.5% CI
THETA(1)	tvV	94.39	2.16	2.29	90.15	98.63
THETA(2)	tvCl	5.11	0.22	4.37	4.68	5.55
THETA(3)	tvV2	97.35	9.00	9.25	79.69	115.01
THETA(4)	tvCl2	7.58	0.22	2.95	7.14	8.01
THETA(5)	dVdWT	0.77	0.06	7.95	0.65	0.90
THETA(6)	dCldWT	0.87	0.07	8.07	0.73	1.01
Source: script.R

Omegas

tableomega <- readRDS("tableomega.RDS")
tableomega

Table Omega
Name	Label	Value	SE	RSE%	Fixed	Diagonal	2.5% CI	97.5% CI	Shrinkage%
OMEGA(1,1)	nV	0.055	0.009	15.881	FALSE	TRUE	0.038	0.073	4.109
OMEGA(2,2)	nCl	0.041	0.008	20.711	FALSE	TRUE	0.024	0.058	6.912
Source: script.R

Sigmas

tablesigma <- readRDS("tablesigma.RDS")
tablesigma

Table Sigma
Name	Label	Value	SE	RSE%	Fixed	2.5% CI	97.5% CI	Shrinkage%
SIGMA(1,1)	CEps	0.127	0.004	2.808	FALSE	0.120	0.134	8.372
Source: script.R

Visual Predictive Check

vpcPlot <- readRDS("vpcPlot.RDS")
vpcPlot

Target Attainment Simulation

Summary

For 100 mg q24h * 7 dose regimen, Cmax and AUC24 on day 7:

97% target achievement for AUC24 Overall; 69% for WT > 115 Kg
92% target achievement for Cmax Overall; 45% for WT > 115 Kg
Consider WT based dose adjustment to maintain target exposures

Plots

Overall

TAplot <- readRDS("TAplot.RDS")
TAplot

Target Attainment by WT Cuts

TAplotbyWT <- readRDS("TAplotbyWT.RDS")
TAplotbyWT

Session Info

sessionInfo()

R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] Certara.RsNLME_1.1.0 flextable_0.6.8     

loaded via a namespace (and not attached):
 [1] sass_0.4.0               tidyr_1.2.0              jsonlite_1.7.3          
 [4] splines_4.1.1            bslib_0.3.1              shiny_1.7.1             
 [7] assertthat_0.2.1         highr_0.9                yaml_2.2.2              
[10] gdtools_0.2.3            shinymaterial_1.2.0      pillar_1.7.0            
[13] backports_1.4.1          lattice_0.20-44          glue_1.6.1              
[16] uuid_0.1-4               digest_0.6.29            RColorBrewer_1.1-2      
[19] promises_1.2.0.1         polyclip_1.10-0          checkmate_2.0.0         
[22] colorspace_2.0-2         plyr_1.8.6               htmltools_0.5.2         
[25] httpuv_1.6.5             Matrix_1.3-4             pkgconfig_2.0.3         
[28] purrr_0.3.4              xtable_1.8-4             scales_1.1.1            
[31] tweenr_1.0.2             later_1.3.0              officer_0.4.0           
[34] ggforce_0.3.3            tibble_3.1.6             mgcv_1.8-36             
[37] generics_0.1.2           farver_2.1.0             ggplot2_3.3.5           
[40] ellipsis_0.3.2           gtsummary_1.5.0          shinyjs_2.1.0           
[43] cli_3.1.1                magrittr_2.0.2           crayon_1.5.0            
[46] mime_0.12                evaluate_0.14            GGally_2.1.2            
[49] fansi_1.0.2              broom.helpers_1.5.0      nlme_3.1-152            
[52] MASS_7.3-54              xml2_1.3.3               tools_4.1.1             
[55] data.table_1.14.2        lifecycle_1.0.1          stringr_1.4.0           
[58] munsell_0.5.0            zip_2.2.0                Certara.NLME8_1.2.0     
[61] compiler_4.1.1           jquerylib_0.1.4          xpose_0.4.13            
[64] systemfonts_1.0.2        rlang_1.0.0              grid_4.1.1              
[67] gt_0.3.1                 rstudioapi_0.13          base64enc_0.1-3         
[70] labeling_0.4.2           rmarkdown_2.11           egg_0.4.5               
[73] gtable_0.3.0             reshape_0.8.8            DBI_1.1.1               
[76] R6_2.5.1                 gridExtra_2.3            knitr_1.37              
[79] dplyr_1.0.7              fastmap_1.1.0.9000       utf8_1.2.2              
[82] commonmark_1.7           Certara.Xpose.NLME_1.1.0 stringi_1.7.6           
[85] Rcpp_1.0.8               vctrs_0.3.8              tidyselect_1.1.1        
[88] xfun_0.29

Data Memo Example

Keith Nieforth & James Craig

February 18, 2022

Notes and Conventions

Notes

Conventions used in this document

Objectives

Study Design

Methods

Overview of the data

Summary

Categorical covariate summary

Continuous covariate summary

Mean profiles Linear

Mean profiles Log

Facet Gender Linear

Facet WT Linear

Base Model Development

Summary

Model Description

GOF Plots

DV vs PRED

DV vs iPRED

CWRES vs TIME

CWRES vs PRED

ETA vs WT

ETA vs SEX

Parameter Estimates

Overall Run Summary

Thetas

Omegas

Sigmas

Visual Predictive Check

Target Attainment Simulation

Summary

Plots

Overall

Target Attainment by WT Cuts

Session Info